feat: Phase 2 - LLM Integration for Canvas

- Add canvas.ts MCP tool with validate_canvas_intent, execute_canvas_intent, interpret_canvas_intent
- Add useCanvasChat.ts bridge hook connecting canvas to chat system
- Update context_builder.py with canvas tool instructions
- Add ExecuteDialog for study name input
- Add ChatPanel for canvas-integrated Claude responses
- Connect AtomizerCanvas to Claude via useCanvasChat

Canvas workflow now:
1. Build graph visually
2. Click Validate/Analyze/Execute
3. Claude processes intent via MCP tools
4. Response shown in integrated chat panel

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

atomizer-dashboard/backend/api/services/context_builder.py

@@ -235,4 +235,12 @@ Available tools:
 - `get_trial_data`, `analyze_convergence`, `compare_trials`, `get_best_design`
 - `generate_report`, `export_data`
 - `explain_physics`, `recommend_method`, `query_extractors`
+
+**Canvas Tools (for visual workflow builder):**
+- `validate_canvas_intent` - Validate a canvas-generated optimization intent
+- `execute_canvas_intent` - Create a study from a canvas intent
+- `interpret_canvas_intent` - Analyze intent and provide recommendations
+
+When you receive a message containing "INTENT:" followed by JSON, this is from the Canvas UI.
+Parse the intent and use the appropriate canvas tool to process it.
 """